# Query-focused Sentence Compression in Linear Time

## TL;DR

This paper presents a linear-time, query-focused sentence compression method that efficiently produces constrained shortenings, significantly faster than ILP-based approaches, improving user interface responsiveness without needing complex solvers or GPUs.

## Contribution

Introduces a novel linear-time transition-based sentence compression technique for query-focused applications, outperforming ILP methods in speed and accuracy.

## Key findings

- Achieves 11X speedup over ILP baselines
- Better reconstructs gold constrained shortenings
- Does not require ILP solver or GPU hardware

## Abstract

Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11X empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU.